{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "2a0b97d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.interpolate import interp2d, interpn\n",
    "from numpy.matlib import repmat\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00f6be88",
   "metadata": {},
   "source": [
    "# Entry/Exit with Homogeneous firms\n",
    "## and translog demand\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "244c912c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sequence_jacobian import solved, simple, create_model\n",
    "\n",
    "@simple\n",
    "def household(w, psi, nu):\n",
    "    L = (w/psi)**nu\n",
    "    return L\n",
    "\n",
    "@solved(unknowns={'v': 1.0, 'N': 1.0}, targets=['valfunc', 'accumulation'])\n",
    "def firms(C, sigma, v, beta, delta, Ne, N, L):\n",
    "    mu      = 1 + 1/(sigma*N)\n",
    "#    y       = p**(-sigma)*C\n",
    "    ell     = L/N            # imposing labor market clearing\n",
    "    pi      = (1-1/mu)*(C/N)\n",
    "    valfunc = v - (pi + beta*(1-delta)*v(1))\n",
    "    accumulation =  N - (1-delta)*N(-1) - Ne\n",
    "    return mu, ell, pi, valfunc, accumulation\n",
    "    \n",
    "@simple \n",
    "def mkt_clearing(mu, N, v, fE, w, Z, C, L, pi, Ne, Nbar):\n",
    "    p       = mu*w/Z\n",
    "    rho     = math.e**(-0.5*(Nbar - N)/(2*Nbar*N))\n",
    "    goods_mkt = p - rho                   # as in BGM\n",
    "    free_entry = v - fE*w/Z\n",
    "    budget     = C - w*L - N*pi\n",
    "    return goods_mkt, free_entry, budget, rho\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0a70a25",
   "metadata": {},
   "source": [
    "### Steady State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "d3fbc144",
   "metadata": {},
   "outputs": [],
   "source": [
    "ces = create_model([household, mkt_clearing, firms], name=\"CES Model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "bb885ca8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2391304347826086"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calibration = {'beta':    0.96,\n",
    "               'psi':     1,\n",
    "               'fE':      1,\n",
    "                'nu': 2.0,\n",
    "                'delta':  0.11,\n",
    "                'sigma':  2,\n",
    "                'w': .75,\n",
    "                'Z': 1,\n",
    "                'Ne': .23, \n",
    "                'C': 1, \n",
    "                'Nbar': 2}\n",
    "\n",
    "ss = ces.steady_state(calibration)\n",
    "ss['mu']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "283c6fca",
   "metadata": {},
   "outputs": [],
   "source": [
    "w0    = .75\n",
    "Nbar0 = 1/(calibration['sigma']*(1/w0-1))\n",
    "Ne0   = calibration['delta']*Nbar0\n",
    "\n",
    "calibration['Ne'] = Ne0\n",
    "\n",
    "ss=ces.solve_steady_state(calibration, \n",
    "      unknowns={'Nbar': Nbar0, 'w': w0, 'Ne': Ne0}, \n",
    "      targets={'rho': 1.0, 'free_entry': 0.0, 'goods_mkt': 0.0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1c603fd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2698147512646427"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss['mu']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d61fdf0c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2e52b213",
   "metadata": {},
   "source": [
    "# Shock to sunk cost of entry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "1d08066a",
   "metadata": {},
   "outputs": [],
   "source": [
    "T = 300\n",
    "dZ = -.03 * .685 ** np.arange(T)\n",
    "irf = {}\n",
    "irf = ces.solve_impulse_linear(ss, ['w', 'Ne', 'C'], ['free_entry', 'goods_mkt', 'budget'], {'fE': dZ})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "c00e2675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tfp = (irf['C'] + ss['C'])/(irf['L'] + ss['L'])\n",
    "fig, axes = plt.subplots(2, 3, figsize=(8, 4))\n",
    "ax = axes.flatten()\n",
    "\n",
    "ax[0].plot(100*((irf['N'][1:10] + ss['N'])/ss['N']-1))\n",
    "ax[0].axhline(0, color='gray', linestyle=':')\n",
    "ax[0].set_title('Mass of establishments')\n",
    "ax[0].set_ylabel('pct deviation from ss')\n",
    "\n",
    "ax[1].plot(100*((irf['mu'][1:10] + ss['mu'])/ss['mu']-1))\n",
    "ax[1].axhline(0, color='gray', linestyle=':')\n",
    "ax[1].set_title('Markup')\n",
    "\n",
    "ax[2].plot(100*((tfp[1:10])/tfp[-1]-1))\n",
    "ax[2].axhline(0, color='gray', linestyle=':')\n",
    "ax[2].set_title('TFP')\n",
    "\n",
    "ax[3].plot(100*((irf['L'][1:10] + ss['L'])/ss['L']-1))\n",
    "ax[3].axhline(0, color='gray', linestyle=':')\n",
    "ax[3].set_title('Employment')\n",
    "\n",
    "ax[4].plot(100*((irf['C'][1:10] + ss['C'])/ss['C']-1))\n",
    "ax[4].axhline(0, color='gray', linestyle=':')\n",
    "ax[4].set_title('Consumption')\n",
    "\n",
    "ax[5].plot(100*((irf['w'][1:10] + ss['w'])/ss['w']-1))\n",
    "ax[5].axhline(0, color='gray', linestyle=':')\n",
    "ax[5].set_title('Wage')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "232e60a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3efc88f310>]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(irf['L'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ec0593fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3eebdf7070>]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = np.arange(1, 1000)\n",
    "plt.plot(np.log(1/N), np.log(1 + 1/(.35*N)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad8c660",
   "metadata": {},
   "source": [
    "# Shock to the cost of entry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5526d421",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
